- As threshold determination must be context-specific, our analytically-straightforward approach should be adopted to leverage prevalence surveys for CAD threshold determination in other settings with a comparable proportion of eligible but not tested participants. [Vanobberghen, F. et al. (2023) . ERJ Open Res 2023]
- The overall performance between CAD4TB v5, v6 and v7 was similar, but the distribution of scores across the 100-point scale varied greatly across the three versions. To accommodate intra-version variation, screening programmes need to select new triaging thresholds for each new software update. [Fehr, J. et. al (2023). IJTLD]
- Newer version (CAD4TB 7) significantly outperformed the predecessor (CAD4TB 6), performing better than human readers and met WHO TPP values. [ZZ. Qin et al., PLOS Digital Health, 2022]
- CAD4TB v7 and two other CAD software solutions emerged from this evaluation as excellent alternatives for human CXR interpretation, performing on par with the Expert Reader and significantly better than the Intermediate Reader [Vietnam: A. Codlin et al., Nature Scientific Report, 2021]
- CAD4TB performs significantly better than human readers [Qin et al., The Lancet Digital Health, 2021] [Tanzania; Breuninger et al., PLOS One, 2014]
- CAD4TB is an accurate tool for community-based TB screening for prevalence survey in Kenya. CAD4TB 6 met the optimal WHO TPP (Target Product Profile) [Kenya: Mungai et al., medRxiv, 2021]
- CAD4TB on par with human expert readers [South Africa; Hogeweg et al., IEEE Trans Med Imaging, 2015], [Tanzania; Steiner et al., Public Health Action, 2015], [Zambia; Melendez et al., IJTLD, 2017], [London; Melendez et al., IJTLD, 2018], [the Philippines; Philipsen et al., IJTLD, 2019], [Pakistan; Murphy et al., Nature Scientific Reports, 2020] [South Africa; Fehr et al., NpJ, 2021], [Tavaziva et al., Clinical Infectious Diseases, 2021]
- CAD4TB met the WHO’s TPP at 90% sensitivity. [Qin et al., The Lancet Digital Health, 2021]
- In a community-based multi-disease screening survey in an HIV-endemic rural area, CAD4TB achieved comparable sensitivity and specificity to the radiologist. CAD4TB has potential to replace radiologists for triaging CXRs in prevalence surveys [South Africa: Fehr et al., npj digital medicine, 2021]
- In Tanzania, CAD4TB performance was significantly better than a clinical officer in [Tanzania: Breuninger et al., PLOS One, 2014]
- Delft Light portable digital X-ray and CAD4TB in parallel with the WHO 4-symptom screen achieved a lower pre-diagnostic loss of presumptive TB cases. The W4SS + portable X-ray with CAD screen-based intervention was more efficient for TB case yield: four times the number of TB cases yield than symptom-only-based screening intervention. [Nigeria: Babayi et al., Public Health Action, 2023]
- CAD may provide viable options to increase TB detection, especially in low-resource areas where there maybe no available expert radiologists. [Gelaw et al., PLOS Global Public Health, 2023]
- Ultra-portable X-rays with CAD were overall well received to decentralise radiological assessment for TB. [Qin et al, PLOS ONE, 2023]
- The per-screen costs for the two CAD software programs that had a perpetual licensing costing structure are considerably lower than the cost with radiologists for high throughput in ACF scenarios. [S. Bashir. Et al., PLOS ONE, 2022]
- TB screening using Delft Light Backpack X-ray and CAD4TB during community-based ACF in hard-to-reach Niger Delta communities of Nigeria showed a high TB prevalence among participants. Nationwide deployment of the instrument in hard-to-reach areas is recommended. [B. Odume. et al., Public Health Action. 2022]
- CAD4TB v7 had a significantly higher AUC than v6, 0.903 (95% CI: 0.897–0.908) compared to 0.823 (0.816–0.830). CAD4TB 7 results in a steep initial increase of Xpert test saved. Until a threshold of 75, greater numbers of Xpert tests can be saved using CAD4TB v7. [ZZ. Qin et al., PLOS Digital Health, 2022]
- Using CAD4TB can save molecular tests such as Xpert tests. [Pakistan: Habib et al., Scientific Reports, 2020], [Qin et al., The Lancet Digital Health, 2021] [Pakistan: Zaidi et al., Nature Scientific Reports, 2018], [Bangladesh: T. Rahman et al., European Respiratory Journal, 2017]
- All five AI algorithms (incl. CAD4TB) significantly outperformed the radiologists and reduced the number of Xpert tests required by 50% while maintaining a sensitivity above 90%. [Qin et al., The Lancet Digital Health, 2021]
- Triage using AI-based CXR interpretation (incl. CAD4TB) can be cost-effective and even cost-saving relative to standard practice among persons with suspected TB in Pakistan. These results are likely applicable to other low-income, high-TB-burden settings. [Nsengiyumva et al. Open Forum Infectious Diseases, 2021]
- Community-based screening with innovative activities, comprising sensitive screening and diagnostic tools, including a digital X-ray and CAD4TB, effectively improves TB case detection. [Pakistan; A. Wali et al., Public Health Action, 2021]
- CAD4TB as a triage tool to pre-screen people with diabetes for Xpert testing can improve case-detection while potentially reducing program costs with more rational use of expensive molecular tests. [Pakistan; Habib et al., Nature Scientific Reports, 2020]
- In Pakistan, CAD4TB as a triage tool could minimize use of expensive molecular tests in low resource settings. [Pakistan; Murphy et al., Nature Scientific Reports, 2020]
- Used as a pre-screening system in TB-endemic regions, CAD4TB allows for testing of much larger numbers of subjects at a fraction of the cost. [Pakistan; Murphy et al., Nature Scientific Reports, 2020]
- In the Philippines, CAD4TB as a second reader showed 22% increase of TB detection and could help detect additional TB-positive cases that were initially missed at limited additional costs. [Philippines: Philipsen et al., IJTLD, 2019]
- In Pakistan, CAD4TB as a triage tool could minimize use of expensive molecular tests in low resource settings. [Pakistan; Zaidi et al., Nature Scientific Reports, 2018]
- Reduces cost per identified TB case and the cost per screened subject by almost half. [Philipsen et al., Nature Scientific Reports, 2015]
- Useful where trained human readers are scarce. [Muyoyeta et al., PLOS One, 2014]
TB/HIV
- In a community-based multi-disease screening survey in an HIV-endemic rural area, CAD4TB achieved comparable sensitivity and specificity to the radiologist. HIV serostatus did not impact CAD4TB’s performance. [South Africa: Fehr et al., npj digital medicine, 2021]
- Digital CXR using CAD4TB with universal HIV screening significantly increased timelines and completeness of HIV and TB diagnosis. [Malawi: MacPherson et al., PLOS MEDICINE, 2021]
- Combined use of mobile X-ray and CAD4TB to identify TB among high-risk groups to improve early TB diagnosis can deliver a major contribution to TB control in Romania. [Romania: B. Mahler et al., BMJ Open, 2021]
- In an HIV-negative population, CAD4TB v6 met WHO-recommended minimal accuracy for pulmonary tuberculosis triage tests. [F.A. Khan et al., The Lancet Digital Health, 2020]
Diabetes
- CAD4TB offers good diagnostic accuracy as triage for TB screening among diabetes patients [Habib et al., Nature Scientific Reports, 2020]
- CAD4TB with X-ray systems allowed not only rapid and systematic triage to Xpert testing, but also found quantitatively more TB-like abnormalities in those with Diabetes Mellitus In Bangladesh [Paul et al., Science Direct, 2020]
- CAD4TB has potential as a triage tool for TB screening in people living with diabetes, thereby significantly reducing the need for microbiological examination in Indonesia [Koesoemadinata et al., IJTLD, 2018]
Migrants
- CAD has the potential to be a useful additional diagnostic tool for paediatric TB by fine-tuning an existing algorithm with as little as several hundred well-classified paediatric chest x-rays. [South Africa: Palmer et al., PLOS Global Public Health, 2023]
Paediatric TB
- The performance of CAD4TB v7 to identify TB in children (<13 years) significantly improved after fine-tuning it with a set of well-characterised paediatric chest x-rays. CAD has the potential to be useful additional diagnostic tool for paediatric tuberculosis. [South Africa: Palmer et al., PLOS Glob Public Health, 2023]
- CAD may provide viable options for use in TB screening programs to increase TB detection, especially in low resource areas where there may be no available expert radiologists. [Gelaw et al., Plos Global Public Health, 2023]
Prison Screening
- Screening by mobile x-ray systems with automated interpretation could reduce the number of confirmatory tests required and enable screening to be more rapid in high burden TB settings, while still maintaining sufficient sensitivity. [Soares et al., The Lancet Regional Health – Americas, 2023]
- Inclusion of digital CXR to systematic TB screening detected additional TB cases among inmates that would otherwise have been missed, and using CAD4TB may also improve performance of screening algorithm. [Kim et al., IJTLD, 2020]
- High uptake of new screening tools such as digital X-ray with CAD4TB may be particularly feasible, reliable, and highly acceptable in prison settings. [Wali et al., BMC Public Health, 2019]
- CAD4TB reliably evaluates CXRs from a mostly asymptomatic prison population, with a performance comparable to local readers in Tanzania. [Steiner et al., Public Health Action, 2015]
Non-TB Abnormalities
- CAD4TB has the potential to simultaneously provide information on other non-TB abnormalities that might be of clinical relevance in communities alongside TB. CAD can be useful for LMICs where there is no routine screening for non-TB abnormalities, and there is often a shortage of qualified radiologists. [Zambia, South Africa: Ngosa, D. et al. (2023). BMC Infect Dis, 2023]
- Improving TB control: efficiencies of case-finding interventions in Nigeria (Public Health Action, 2023)
- Diagnostic accuracy of three computer-aided detection systems for detection pulmonary tuberculosis on chest radiography when used for screening: Analysis of an international, multicenter migrants screening study (PLOS Glob Public Health, 2023)
- Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children (PLOS Glob Public Health, 2023)
- The performance of computer-aided detection digital chest X-ray reading technologies for triage of active Tuberculosis among persons with a history of previous Tuberculosis (Clinical Infectious Diseases. 2023)
- Portable digital X-ray for TB pre-diagnosis screening in rural communities in Nigeria (Public Health Action, 2022)
- Population-wide active case finding and prevention for tuberculosis and leprosy elimination in Kiribati: the PEARL study protocol (BMJ Open, 2022)
- Integrated screening and testing for TB and COvid-19 in Peru (Public Health Action, 2022)
- Diagnostic accuracy of computer aided reading of chest x-ray in screening for pulmonary tuberculosis in comparison with Gene-Xpert (Pakistan Journal of Medical Sciences, 2022)
- Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence-Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis (Open Forum Infectious Diseases, 2021)
- Accuracy of computer-aided chest X-ray in community-based tuberculosis screening: Lessons from the 2016 Kenya National tuberculosis Prevalence Survey (PLOS Global Public Health, 2022)
- Early TB case detection by community-based mobile X-ray screening and Xpert testing in Balochistan (Public Health Action, 2021)
- Computer-aided X-ray screening for tuberculosis and HIV testing among adults with cough in Malawi (the PROSPECT study): A randomised trial and cost-effectiveness analysis (PLOS Medicine, 2021)
- Use of targeted mobile X-ray screening and computer-aided detection software to identify tuberculosis among high-risk groups in Romania: descriptive results of the E-DETECT TB active case-finding project (BMJ Open, 2021)
- Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa (npj Digital Medicine, 2021)
- Automated chest radiography and mass systematic screening for tuberculosis (The International Journal of Tuberculosis and Lung Disease, 2020)
- Evaluation of computer aided detection of tuberculosis on chest radiography among people with diabetes in Karachi Pakistan (Nature Scientific Report, 2020)
- Computer Aided Detection of Tuberculosis on Chest Radiographs: An Evaluation of the CAD4TB v6 system (Nature Scientific Report, 2020)
- Symptom and Digital Chest X-ray TB Screening in South Africa Prisons: Yield and Cost Effectiveness (The International Journal of Tuberculosis and Lung Disease, 2020)
- A Public-Private Model to Scale Up Diabetes Mellitus Screening Among People Accessing Tuberculosis Diagnostics in Dhaka, Bangladesh (The International Journal of Tuberculosis and Lung Disease, 2020)
- Yield, Efficiency and Costs of Mass Screening Algorithms for Tuberculosis in Brazilian Prisons (Clinical infectious diseases, 2020)
- Deep learning, Computer-Aided Radiography Reading for Tuberculosis: a Diagnostic Accuracy Study from a Tertiary Hospital in India (Nature Scientific Reports, 2020)
- Prevalence of Tuberculosis, HIV/AIDS, and Hepatitis; in a Prison of Balochistan: a Cross-Sectional Survey (BMC public health, 2019)
- Automated Chest X-ray Reading for Tuberculosis in the Philippines to Improve Case Detection: a Cohort Study (The International Journal of Tuberculosis and Lung Disease, 2019)
- Computer-Assisted Chest Radiography Reading for Tuberculosis Screening in People Living with Diabetes Mellitus (The International Journal of Tuberculosis and Lung Disease, 2018)
- Evaluation of the Diagnostic Accuracy of Computer-Aided Detection of Tuberculosis on Chest Radiography Among Private Sector Patients in Pakistan (Nature Scientific Reports, 2018)
- Accuracy of an Automated System for Tuberculosis Detection on Chest Radiographs in High-risk Screening (The International Journal of Tuberculosis and Lung Disease, 2018)
- Computer-Aided Reading of Tuberculosis Chest Radiography: Moving the Research Agenda Forward to Inform Policy (European Respiratory Journal, 2017)
- Automatic Versus Human Reading of Chest X-rays in the Zambia National Tuberculosis Prevalence Survey (The International Journal of Tuberculosis and Lung Disease, 2017)
- Digital CXR with Computer-Aided Diagnosis Versus Symptom Screen to Define Presumptive Tuberculosis Among Households Contacts and Impact on Tuberculosis Diagnosis (BMC Infectious Diseases, 2017)
- An Evaluation of Automated Chest Radiography Reading Software for Tuberculosis Screening Among Public- and Private-sector Patients (European Respiratory Journal, 2017)
- An Automated Tuberculosis Screening Strategy Combining X-ray Based Computer-Aided Detection and Clinical Information (Nature Scientific Reports, 2016)
- Screening for Pulmonary Tuberculosis in a Tanzanian Prison and Computer-Aided Interpretation of Chest X-rays (Public Health Action, 2015)
- Automated Chest-radiography as a Triage for Xpert Testing in Resource-Constrained Settings: a Prospective Study of Diagnostic Accuracy and Costs (Nature Scientific Reports, 2015)
- Computerized Reading of Chest Radiographs in The Gambia National tuberculosis Prevalence Survey: Retrospective Comparison with Human Experts (Proceeding from Union World Conference on Lung Health, 2015)
- Objective Computerized Chest Radiography Screening to Detect Tuberculosis in the Philippines (Proceeding from Union World Conference on Lung Health, 2015)
- Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa (PLOS one, 2014)
- The Sensitivity and Specificity of Using a Computer Aided Diagnosis Program for Automatically Scoring Chest X-Rays of Presumptive TB Patients Compared with Xpert MTB/RIF in Lusaka Zambia (PLOS one, 2014)
- Detection of Chest X-ray abnormalities and tuberculosis using computer-aided detection vs interpretation by radiologists and a clinical offer (Proceeding from Union World Conference on Lung Health, 2014)
- Detection of Tuberculosis Using Digital Chest Radiography: Automated Reading vs. Interpretation by Clinical Officers (The International Journal of Tuberculosis and Lung Disease, European respiratory Journal, 2013)
- Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis (BMC Global Public Health, 2023)
- Computer-aided detection thresholds for digital chest x-ray interpretation in tuberculosis diagnostic algorithms (ERJ Open Research, 2023)
- Evaluation of tuberculosis diagnostic test accuracy using Bayesian latent class analysis in the presence of conditional dependence between the diagnostic tests used in a community-based tuberculosis screening study (PLOS one, 2023)
- CAD4TB software updates: different triaging thresholds require caution by users and regulation by authorities (The International Journal of Tuberculosis and Lund Disease, 2023)
- Evaluation of chest X-ray with automated interpretation algorithms for mass tuberculosis screening in prisons: A cross-sectional study (The Lancet Regional Health Americas, 2023)
- Early user experience and lessons learned using ultra-portable digital X-ray with computer-aided detection (DXR-CAD) products: A qualitative study from the perspective of healthcare providers (PLOS one, 2023)
- Economic analysis of different throughput scenarios and implementation strategies of computer-aided detection software as a screening and triage test for pulmonary TB (PLOS one, 2022)
- Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review (Journal of Medical Systems, 2022)
- Computer-Aided Detection of Tuberculosis from Chest Radiographs in TB Prevalence Survey: External Validation and Modelled Impacts on Commercially Available Artificial Intelligence Software (SSRN, 2022)
- Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis (PLOS Digital Health, 2022)
- “Similar performances but markedly different triaging thresholds in three CAD4TB versions risk systematic errors in TB screening programs” (MedRxiv, 2022)
- Diagnostic accuracy of chest X-ray interpretation for tuberculosis by three artificial intelligence-based software in a screening use-case: an individual patient meta-analysis of global data (MedRxiv, 2022)
- Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis (Nature Scientific Reports, 2021)
- Costs and cost-effectiveness of a comprehensive tuberculosis case finding strategy in Zambia (PLOS one, 2021)
- Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: and evaluation of five artificial intelligence algorithms (The Lancet Digital Health, 2021)
- Chest X-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: an individual patient data meta-analysis of diagnostic accuracy (Clinical Infectious Disease, 2021)
- Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest X-rays? An evaluation of five AI products for TB screening and triaging in a high TB burden setting (ArXiv, 2021)
- Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening (Chronic Diseases and Translational Medicine, 2021)
- A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers (Tuberculosis, 2021)
- Chest X-ray Analysis with Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: a Prospective Study of Diagnostic Accuracy for Culture-Confirmed Disease (The Lancet Digital Health, 2020)
- Can Artificial Intelligence Be Used to Accurately Detect Tuberculosis (TB) from Chest X-ray? A Multi-Platform Evaluation of Five AI Products Used for TB Screening in a High-Burden setting (ArXiv, 2020)
- Using Artificial Intelligence to Read Chest Radiographs for Tuberculosis Detection: A Multi-Site Evaluation of the Diagnostic Accuracy of Three Deep Learning Systems (Nature Scientific Reports, 2019)
- A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest X-rays for pulmonary tuberculosis (PLOS one, 2019)
- Fast and Effective Quantification of Symmetry in Medical images for Pathology Detection: Application to Chest Radiography (Medical Physics, 2017)
- Automatic Detection of Pleural Effusion in Chest Radiographs (Medical Image Analysis, 2016)
- Computer-Aided Detection of Pulmonary Tuberculosis on Digital Chest Radiographs: a Systematic Review (The International Journal of Tuberculosis and Lung Disease, 2016)
- On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis (IEEE Transactions on Medical Imaging, 2015)
- Localized energy-based normalization of medical images: application to chest radiography (IEEE Transactions on Medical Imaging, 2015)
- Automatic Detection of Tuberculosis in Chest Radiographs Using a Combination of Textural, Focal, and shape Abnormality Analysis (IEEE Transactions on Medical Imaging, 2015)
- A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays (IEEE Transactions of Medical Imaging, 2014)
- Cavity Contour Segmentation in Chest Radiographs using Supervised Learning and Dynamic Programming (Medical Physics, 2014)
- Multiple-instance learning for computer-aided detection of tuberculosis (Medical Imaging, 2014)
- Suppression of Translucent Elongated Structures: Applications in Chest Radiography (IEEE Transactions on Medical Imaging, 2013)
- Foreign Object Detection and Removal to Improve Automated Analysis of Chest Radiographs (Medical Physics, 2013)
- Automated Localization of Costophrenic Recesses and Costophrenic Angle Measurement on Frontal Chest Radiographs (Proceeding from SPIE Medical Imaging 2013)
- Improved Texture Analysis for Automatic Detection of Tuberculosis (TB) on Chest Radiographs with Bone Suppression Images (Proceeding from SPIE Medical Imaging 2013)
- Clavicle segmentation in chest radiographs (Medical Image Analysis, 2012)
- Fusion of local and global detection systems to detect tuberculosis in chest radiographs (Medical Image Computing and Computer-Assisted Intervention,2 010)
- Rib Suppression in Chest Radiographs to Improve Classification of Textural Abnormalities (Proceeding from SPIE Medical Imaging 2010)
- Dissimilarity-based Classification in the Absence of Local Ground Truth: Application to the Diagnostic Interpretation of Chest Radiographs (Pattern Recognition, 2009)
- Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography (Medical Physics, 2007)
- Segmentation of Anatomical Structures in Chest Radiographs Using Supervised Methods: a Comparative Study on a Public Databased (Medical Image Analysis, 2006)
- Automatic Detection of Abnormalities in Chest Radiographs Using Local Texture Analysis (IEEE Transactions on medical Imaging, 2002)
- Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines (Int. J. Environ. Res. Public Health, 2022)
- Computer-Aided Detection for Tuberculosis and Silicosis in Chest Radiographs of Gold Miners of South Africa (International Journal of TB and Lund Disease, 2020)
Others
- Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians (Nature Medicine, 2023)
- Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis: Effects on moving health technologies in global health (Social Science & Medicine, 2023)
- The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination (The International Journal of Tuberculosis and Lung Disease, 2023)
- Conditions required for the artificial-intelligence-based computer-aided detection of tuberculosis to attain its global health potential (The Lancet Digital Health, 2022)
- User perspectives on the use of X-rays and computer-aided detection for TB (The International Journal of Tuberculosis and Lung Disease, 2022)
Non-TB Abnormalities
- COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests (Nature Scientific Reports, 2023)
- Assessment of non-tuberculosis abnormalities on digital chest x-rays with high CAD4TB scores form a tuberculosis prevalence survey in Zambia and South Africa (BMC Infectious Diseases, 2023)
CAD for TB Screening: Policies and Guidelines
- Tuberculosis Prevention and Care Among Refugees and Other Populations in Humanitarian Settings: an interagency field guide (CDC, UNHCR, World Health Organization, 2022)
- Screening and Triage for TB using Computer-Aided Detection (CAD) Technology and Ultra-portable X-ray Systems: A Practical Guide (StopTB Partnership, 2021)
- Programmatic innovations to address challenges in tuberculosis prevention and care during the COVID-19 pandemic (World Health Organization, 2021)
- Determining the local calibration of computer-assisted detection (CAD) thresholds and other parameters: a toolkit to support the effective use of CAD for TB screening (World Health Organization, 2021)
- Digital Chest Radiography and Computer-Aided Detection (CAD) Solutions for Tuberculosis Diagnostics – Technology Landscape Analysis (FIND, 2021)
- WHO Consolidated guidelines on tuberculosis – Module 2: Screening Systematic screening for tuberculosis disease (World Health Organization, 2021)
- WHO operational handbook on tuberculosis – Module 2: Screening Systematic screening for tuberculosis disease (World Health Organization, 2021)
- StopTB Partnership Field Guide on Chest X-ray Screening (StopTB Partnership, 2019)
- Mobile Care for TB Screening and Diagnosis – a How-To Guide (USAID/ChallengeTB, 2018)
- Global investments in Tuberculosis research and development: past, present and future (World Health Organization – Policy Paper for the first WHO Global Ministerial Conference on Ending Tuberculosis in the Sustainable Development Era: A Multi-Sectoral Response, 2017)
- Chest Radiography in Tuberculosis Detection – Summary of Current WHO Recommendations and Guidance on Programmatic Approaches (World Health Organization, 2015)
- WHO Compendium of Innovative Health Technologies for Low-Resource Settings (World Health Organization – Compendium of Innovative Technologies, 2015)
- Tuberculosis – Diagnostics Technology and Market Landscape (UNITAID/World Health Organization, 2014)
- Digital Imaging Innovations for Early TB Case Detection (StopTBPartnership/CheckTB, 2012)